---
base_model: google/gemma-4-e2b-it
tags:
- text-generation-inference
- transformers
- gemma4
- peft
- lora
- cybersecurity
- ai-security
- llm-security
- prompt-injection
- cybersecurity
- machine-learning
license: apache-2.0
language:
- en
---

# Gemma 4 E2B — AI & LLM Security Expert

A QLoRA fine-tuned version of [Gemma 4 E2B Instruct](https://huggingface.co/google/gemma-4-e2b-it) specialized in **ai & llm security**.
Specialized in **AI and LLM security**: prompt injection attacks, jailbreaks, model poisoning, training data extraction, adversarial examples, and guardrail design.

Part of the [rezaduty cybersecurity model family](https://huggingface.co/rezaduty).

---

## Expertise

- Prompt injection — direct and indirect attack vectors
- Jailbreak techniques and system prompt extraction
- Training data poisoning and backdoor attacks
- Membership inference and model inversion attacks
- LLM guardrails, content filtering, and output validation
- Secure RAG pipelines and agentic system threat modeling

---

## Model Details

| Property | Value |
|---|---|
| **Base model** | google/gemma-4-e2b-it (2B parameters) |
| **Fine-tuning method** | QLoRA (rank 16, α 16) |
| **Domain** | AI & LLM Security |
| **License** | Apache 2.0 |

---

## Usage

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

base_model = "google/gemma-4-e2b-it"
adapter    = "rezaduty/gemma4-e2b-ai-llm-security"

tokenizer = AutoTokenizer.from_pretrained(adapter)
model = AutoModelForCausalLM.from_pretrained(
    base_model, torch_dtype=torch.bfloat16, device_map="auto"
)
model = PeftModel.from_pretrained(model, adapter)

messages = [
    {"role": "system", "content": [{"type": "text", "text": "You are an expert in AI and LLM security. You provide deep answers on prompt injection, model poisoning, adversarial attacks, LLM guardrails, and secure AI deployment."}]},
    {"role": "user",   "content": [{"type": "text", "text": "Your question here"}]},
]
inputs = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
output = model.generate(inputs, max_new_tokens=512, temperature=0.7, top_p=0.9)
print(tokenizer.decode(output[0][inputs.shape[-1]:], skip_special_tokens=True))
```

---

## System Prompt

```
You are an expert in AI and LLM security. You provide deep answers on prompt injection, model poisoning, adversarial attacks, LLM guardrails, and secure AI deployment.
```

---

## See Also

- [General cybersecurity model](https://huggingface.co/rezaduty/gemma4-e2b-cybersecurity-interview) — full 646-example dataset
- [Docker & Container Security](https://huggingface.co/rezaduty/gemma4-e2b-docker-container-security)
- [All rezaduty models](https://huggingface.co/rezaduty)